A.1 Multiple data matrices A combinatorial optimization problem can be presented with multiple (f) relationship features between two groups of items. In FFSP, for example, a production cost could be different for each process that one has to take into account for scheduling in addition to the processing time for each pair of the job and the machine. When there are f number of matrices that need to be encoded (D1, D2, ..., Df), MatNet can be easily expended to accommodate such problems by using the mixed-score attention shown in Figure A.1 instead of the one in Figure 2(b). "Trainable element-wise function" block in Figure A.1 is now an MLP with f + 1 input nodes and 1 output node. A.2 Alternative encoding sequences Equation (2) in the main text describes the application of FA and FB in the graph attentional layer of MatNet that happens in parallel.

A ubiquitous learning problem in today's digital market is, during repeated interactions between a seller and a buyer, how a seller can gradually learn optimal pricing decisions based on the buyer's past purchase responses. A fundamental challenge of learning in such a strategic setup is that the buyer will naturally have incentives to manipulate his responses in order to induce more favorable learning outcomes for him. To understand the limits of the seller's learning when facing such a strategic and possibly manipulative buyer, we study a natural yet powerful buyer manipulation strategy. That is, before the pricing game starts, the buyer simply commits to "imitate" a different value function by pretending to always react optimally according to this imitative value function. We fully characterize the optimal imitative value function that the buyer should imitate as well as the resultant seller revenue and buyer surplus under this optimal buyer manipulation. Our characterizations reveal many useful insights about what happens at equilibrium. For example, a seller with concave production cost will obtain essentially 0 revenue at equilibrium whereas the revenue for a seller with convex production cost is the Bregman divergence of her cost function between no production and certain production. Finally, and importantly, we show that a more powerful class of pricing schemes does not necessarily increase, in fact, may be harmful to, the seller's revenue. Our results not only lead to an effective prescriptive way for buyers to manipulate learning algorithms but also shed lights on the limits of what a seller can really achieve when pricing in the dark.

Regulatory frameworks, such as the EU AI Act, encourage openness of general-purpose AI models by offering legal exemptions for "open-source" models. Despite this legislative attention on openness, the definition of open-source foundation models remains ambiguous. This paper models the strategic interactions among the creator of a general-purpose model (the generalist) and the entity that fine-tunes the general-purpose model to a specialized domain or task (the specialist), in response to regulatory requirements on model openness. We present a stylized model of the regulator's choice of an open-source definition to evaluate which AI openness standards will establish appropriate economic incentives for developers. Our results characterize market equilibria -- specifically, upstream model release decisions and downstream fine-tuning efforts -- under various openness regulations and present a range of effective regulatory penalties and open-source thresholds. Overall, we find the model's baseline performance determines when increasing the regulatory penalty vs. the open-source threshold will significantly alter the generalist's release strategy. Our model provides a theoretical foundation for AI governance decisions around openness and enables evaluation and refinement of practical open-source policies.

In the digital economy, technological innovations make it cheaper to produce high-quality content. For example, generative AI tools reduce costs for creators who develop content to be distributed online, but can also reduce production costs for the users who consume that content. These innovations can thus lead to disintermediation, since consumers may choose to use these technologies directly, bypassing intermediaries. To investigate when technological improvements lead to disintermediation, we study a game with an intermediary, suppliers of a production technology, and consumers. First, we show disintermediation occurs whenever production costs are too high or too low. We then investigate the consequences of disintermediation for welfare and content quality at equilibrium. While the intermediary is welfare-improving, the intermediary extracts all gains to social welfare and its presence can raise or lower content quality. We further analyze how disintermediation is affected by the level of competition between suppliers and the intermediary's fee structure. More broadly, our results take a step towards assessing how production technology innovations affect the survival of intermediaries and impact the digital economy.

A ubiquitous learning problem in today's digital market is, during repeated interactions between a seller and a buyer, how a seller can gradually learn optimal pricing decisions based on the buyer's past purchase responses. A fundamental challenge of learning in such a strategic setup is that the buyer will naturally have incentives to manipulate his responses in order to induce more favorable learning outcomes for him. To understand the limits of the seller's learning when facing such a strategic and possibly manipulative buyer, we study a natural yet powerful buyer manipulation strategy. That is, before the pricing game starts, the buyer simply commits to "imitate" a different value function by pretending to always react optimally according to this imitative value function. We fully characterize the optimal imitative value function that the buyer should imitate as well as the resultant seller revenue and buyer surplus under this optimal buyer manipulation.

Blockchain enables creators to package and monetize their digital content in new ways. However, it's not the only tech stack that is doing so. Artificial intelligence (AI) is also redefining creativity in the digital space, and here's how. DALL-E, Stable Diffusion, and Midjourney are all generative AI models. They use AI algorithms to automatically generate digital content based on a simple prompt that would otherwise take a human a long time to complete.

Baidu, the Chinese search engine giant that has plowed money into AI and autonomous vehicle technology, unveiled Wednesday a new all-electric robotaxi that it plans to deploy at scale across China. Baidu will add the Apollo RT6 EV -- a cross between an SUV and a minivan that comes with a detachable steering wheel -- to its Apollo Go ride-hailing service next year. The new battery-electric robotaxi is Baidu's sixth-generation autonomous vehicle and the first model built on its Xinghe self-driving platform. The automaker said that developing the battery-electric architecture in-house helped trim production costs to a manageable $37,000 per unit. Baidu said that recent advances in manufacturing have cut production costs, allowing it to eventually build and operate tens of thousands of robotaxis at scale across Chinese megacities including Beijing, Shanghai, Guangzhou and Shenzhen by next year.